You bought the AI tool. Are your engineers using it?

LeadDev's AI Impact Report 2026, based on a survey of nearly 600 respondents, found that enterprise adoption of AI coding tools is led by Claude Code at 78%, but daily active use drops to 50%, with GitHub Copilot at 56% adoption but only 14% daily use. Only 26% of leaders surveyed saw a significant productivity boost, and just 31% of organizations measure the impact, up from 18% last year. Dan Moore, senior director at FusionAuth, called the gap a 'procurement problem masquerading as an adoption problem,' noting engineers often bypass approved tools to find better ones.

This is your last article that you can read this month before you need to register /register a free LeadDev.com account. Estimated reading time: 3 minutes Key takeaways: Claude Code leads adoption at 78% , but daily use drops to 50% . One leader calls it a procurement problem disguised as an adoption problem with engineers going around what got approved to find something better. GitHub says 88% of Copilot users feel more productive . Only 26% of leaders surveyed for LeadDev’s report saw a real gain, and just 31% of organizations are even measuring impact.- The real bottleneck is waiting for tests, builds, and deploys . Since the launch of OpenAI’s ChatGPT https://leaddev.com/technical-direction/meet-speaker-evan-morikawa-how-openai-scaled-chatgpt in November 2022, generative AI tools like GitHub Copilot and Cursor https://leaddev.com/technical-direction/how-jit-overcame-developer-resistance-shift-cursor have quickly become staples in many developers’ workflows. Their widespread adoption has fueled widespread AI hype – even in boardrooms https://www.computerweekly.com/news/366554314/Gartner-Execs-put-generative-AI-on-business-agenda?utm source=chatgpt.com – about AI’s potential to significantly boost engineering productivity. However, LeadDev’s AI Impact Report 2026 https://leaddev.com/the-ai-impact-report-2026 found that buying an AI tool https://leaddev.com/ai/best-ai-coding-assistants doesn’t mean engineers will make it part of their daily workflow. According to the report, based on a survey of nearly 600 respondents, enterprise adoption is led by Claude Code https://leaddev.com/ai/why-microsoft-engineers-are-using-claude-code at 78%, followed by Claude model/chat at 64% and GitHub Copilot at 56%. Yet being approved and funded doesn’t guarantee daily dominance: when asked which tool sees the most active use, Claude Code drops to 50%, with Claude model/chat at 18% and GitHub Copilot at 14%. Your inbox, upgraded. Receive weekly engineering insights to level up your leadership approach. Dan Moore, senior director, CIAM strategy and identity standards at FusionAuth,, highlights an important distinction in the report: being “adopted” doesn’t necessarily mean a tool is being “paid for consistently.” “That gap is a procurement problem masquerading as an adoption problem https://leaddev.com/ai/ai-adoption-has-to-be-driven-from-the-top ,” he adds. “The tools engineers are gravitating toward https://leaddev.com/ai/your-ai-coding-tools-buying-checklist-for-2026 aren’t always the ones the organization funded. When you see that pattern, the right question isn’t ‘why aren’t engineers using what we approved?’ It’s ‘why are engineers going around what we approved to find something better?’” The AI hype wave The tools companies buy in frequently miss the mark because they don’t truly understand the day-to-day nature of engineering tasks. According to GitHub, https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ 88% of Copilot users feel more productive when using the coding assistant. Anthropic’s own engineers reported https://www.anthropic.com/research/how-ai-is-transforming-work-at-anthropic? bhlid=9b8836c5e2ca155c2d8a3894cd74c5847cfd7aa1&utm source= a 50% productivity boost when using Claude, while the company saw a 67% increase in merged pull requests per engineer per day after adopting Claude Code https://www.anthropic.com/research/81k-economics? bhlid=f2d4a41daac722df4c87ebd91b627dfd936bd352&utm source= . However, only 26% surveyed for LeadDev’s report found a significant boost in productivity. https://leaddev.com/velocity/productivity-isnt-always-fast Just 31% of organizations measure the impact of AI-powered developer tools, up from 18% last year. While that’s a step forward, most are still making decisions without hard evidence. Another 48% are working out how to measure success, suggesting the tools are being adopted faster than organizations can assess their value. “The bottlenecks that we tend to see at companies are not in the hands-on keyboard time; it is in the time waiting for the test to pass or fail, or for a build or deploy that won’t happen for another two to three days,” says Rebecca Murphey https://leaddev.com/community/rebecca-murphey , field CTO of Swarmia. “There’s a considerable and genuine need for AI tools within teams, but there’s a real detachment from the software development lifecycle SDLC https://leaddev.com/career-development/using-experiments-bring-security-your-software-development-life-cycle they’re meant to help, and there’s still a lot to learn and distribute about how to best use AI tools,” says Andrew Zigler, senior developer advocate at LinearB. Moore echoes this sentiment. “If you can’t name the problem you’re solving, measure it, and prove the tool improved it, you’re just riding the hype wave.” More like this Ask your devs about AI tools AI adoption starts in the boardroom https://leaddev.com/ai/ai-adoption-has-to-be-driven-from-the-top – but succeeds with developers. To address core challenges effectively, engineers need to be included in discussions with leadership about implementing AI tools. This collaboration helps determine the appropriate use cases for AI within the organization and the specific problems it should aim to solve. Moore bets that most AI tooling decisions are made by engineering leaders https://leaddev.com/the-engineering-leadership-report-2026/ who aren’t explicitly thinking about the pipeline. “Did anyone ask the developers? If so, they would have gotten different input than a procurement evaluation aimed at increasing development velocity. I’d go so far as to say software engineers must be involved in choosing AI tools their organizations invest in.” Zigler agrees. He argues that software engineers should be closely involved in this process, particularly when it comes to identifying and explaining bottlenecks. Whether they need systems to slow down or speed up, more tokens https://leaddev.com/reporting/the-tokenmaxxing-hype-didnt-last-long , or access to different tools, engineers should have clear channels for raising these needs and escalating requests.